Extraction Method Research Articles

Research progress on extraction, separation, structure, and biological activities of polysaccharides from the genus Atractylodes: A review.

Plants of the genus Atractylodes are perennial herbaceous plants in the family Asteraceae, whose rhizome is often used in the production of medicines and health products. There are 6 main species in this genus, namely A. macrocephala, A. lancea, A. chinensis, A. carlinoides, A. koreana and A. japonica. Among them, A. lancea and A. macrocephala are the most extensively investigated. Polysaccharides as the main active ingredients extracted and isolated from plants in this genus, show good pharmacological activities in vivo and in vitro, such as immunomodulatory, antioxidant, antidiabetic and intestinal protective activities. The pharmacological activities of polysaccharides are closely related to their extraction methods and physicochemical properties. This article discusses the extraction and separation methods, molecular weight, monosaccharide composition, chemical structure characteristics and pharmacological activities of polysaccharides from the genus Atractylodes. Furthermore, a comparative analysis of the relationship of monosaccharide composition, relative molecular weight and structural modifications with the pharmacological activities of polysaccharides of the genus Atractylodes was carried out, which provided a reference for the development and utility of polysaccharides.

Extraction of antioxidant and antibacterial agents from avocado (Persea americana) seed using Pulsed Electric Field method

The abundance of avocado seeds causes environmental problems. Therefore, avocado seeds, which are high in bioactive compounds, have been utilized. Several extraction methods have been explored to maximize this utilization. However, conventional extraction methods present major challenges due to disadvantages such as requiring high temperatures, high energy consumption, long extraction times, and producing solvent waste. To that purpose, the Pulse electric field (PEF) extraction method was developed, which is highly efficient, has a short extraction process, is environmentally friendly, and does not produce solvent waste. This study observed the effect of electric field strength (2.5–7.5 kV/cm) and pulse frequency (100–250 Hz) until an optimum point on the properties of avocado seed extract (Avs) as an antioxidant and antibacterial agent. The results show that increasing the electric field strength and pulse frequency boosts the extraction yield and total phenolic content (TPC). This is highly supported by SEM images, which show more pores, cavities, and disrupted membrane cells as the electric field strength and pulse frequency increased. GC-MS and FTIR analysis also showed that the extract contains bioactive compounds such as polyphenols, fatty acids, and other compounds with the representing peaks OH, C−H stretch, CC, −C−H stretch, C−O stretch, and in-plan deformation vibration CH indicating as antioxidant and antibacterial agents. The extract obtained at 7.5 kV/cm with a pulse frequency of 250 Hz shows the best performance of bioactive potential indicated by antioxidant activity, IC50, and surface area of the inhibition zone against E. coli and S. aureus, which are 95.56 %, 90 µg/mL, 396 mm2, and 785 mm2, respectively. The inhibitory zones of the two microbials were relatively stable until the seventh day of observation. This research showed that the EF extraction method serves as an eco-extraction technology of natural bioactive compounds, which has high potential application as an antioxidant and antibacterial agent.

Evaluation of DNA Extraction Methods for Microbial Community Profiling in Deadwood Decomposition.

As technologies advance alongside metabarcoding and metagenomic resources, particularly for larger fungal genomes, DNA extraction methods must be optimized to meet higher thresholds, especially from complex environmental substrates. This study focused on extracting fungal genomic compounds from woody substrates, a challenge due to the embedment of endophytic and saprotrophic fungi within wood cells, the physical recalcitrance of wood, the adsorption of nucleic acids to wood polymers, and the release of downstream inhibitors. Hypothesizing that cetyltrimethylammonium bromide would be the best option, we compared prominent methods by extracting and sequencing microbial DNA from sound and decayed birch (Betula papyrifera) and pine (Pinus resinosa). DNA quantities varied significantly depending on extraction methods and decay stage. The quality of DNA, in terms of purity and integrity, significantly impacted whether the samples could be amplified and sequenced. However, amplicon sequencing of bacterial and fungal communities revealed no significant extraction bias. This, along with the sequencing effectiveness and cost/time efficiency, indicates that Qiagen is the gold standard for woody substrates. This study increases confidence in published amplicon data sets regardless of the extraction methods, provides a cost-benefit table for making protocol decisions, and offers guidance on fungal DNA extractions from complex organic substrates (sound and decayed wood) that would best suit future metagenomic efforts.

A Descriptive Analysis of the Nutraceutical Constituents, Extraction Techniques, and Possible Health Benefits of Tamarind

Background: Tamarind is a multifunctional tree, with nearly every portion having significant nutritional or therapeutic use. The fruit, which produces acidic pulp, is the most valuable and often utilized portion. Objective: The aim of this paper is to discuss the distribution, habitat, chemical constituents and extraction techniques of tamarind. Methods: This study reviews the chemical constituents, health benefits and extraction techniques of tamarind. Databases such as PubMed, Scopus, Science Direct, and Google Scholar were used to search articles from 1990 to 2022. The key search terms included tamarind, phytoconstituents extraction and anti-nutritional factor. Research studies also included similar plant parts extracted from similar solvents which were considered in this review. Results: According to our observations, Tamarindus indica is a crucial plant with significant pharmacological properties. It is extensively used as a conventional medicine to treat a variety of disorders; however, its active ingredients require further investigation for future drug development and pharmacological activity. Tamarind contains a diverse range of bioactive compounds, including polyphenols, flavonoids, alkaloids, vitamins, and minerals, which have been associated with various health-promoting properties. The study examines different extraction methods used to isolate tamarind's bioactive constituents, such as solvent extraction, supercritical fluid extraction (SFE), and microwave-assisted extraction (MAE). Furthermore, the research highlights the potential health benefits of tamarind, including antioxidant activity, anti-inflammatory effects, gastrointestinal support, cardiovascular benefits, and antimicrobial properties. Discussion: Tamarind includes citric acid, linoleic acid, volatile oils, vitamin C, potassium, Camp sterol, -amyrin, Tannins, saponins, and glycosides as a result of this. It has several pharmacological properties such as reducing swelling, preventing oxidation, hypolipidemic, weight loss, antibacterial, hepatoprotective, anthelmintic, and painkiller properties. These properties will assist to generate interest in Tamarind and develop novel preparations with more medicinal and commercial potential. The findings indicate that tamarind holds significant promise as a natural resource for developing functional foods, nutraceuticals, and pharmaceuticals with potential positive impacts on human health.

Enhanced analysis of PAHs in tea using GC–MS/MS with hydrogen carrier gas

A green multiresidue method for PAH analysis was developed using hydrogen as the carrier gas, avoiding harmful solvents and integrating waste-reducing strategies such as µSPE. The use of hydrogen carrier gas in gas chromatography coupled with tandem mass spectrometry (GC–MS/MS) was explored for the enhanced detection of polycyclic aromatic hydrocarbons (PAHs) in black tea. The study demonstrated that hydrogen, as a carrier gas, offers better chromatographic properties than helium or nitrogen. This leads to improved separation of critical pairs using a short run time method. A fast extraction method using micro solid-phase extraction (µSPE) as a clean-up step was developed to analyze 24 PAHs. The extraction method was optimized using a 4 mL 1:1 mixture of ethyl acetate and acetone. The method was validated and demonstrated good recoveries, ranging from 73 % to 113 % for most PAHs, with lower recoveries (50–63 %) observed for high molecular weight PAHs. Reproducibility was also high, with relative standard deviation (RSD) values below 15 %. Linearity and matrix effects were also evaluated, yielding excellent results, with no PAH exhibiting strong matrix effects. Real sample analysis of black tea from various brands and origins revealed the presence of multiple PAHs with concentrations ranging 9.5–39.2 µg/Kg, some exceeding the maximum residue levels set by the European Union. These findings highlight the need for efficient methodologies to monitor PAHs in food products.

Assessing the Effect of Minimally Invasive Lipid Extraction on Parchment Integrity by Artificial Ageing and Integrated Analytical Techniques.

To assess the short and long-term effect of a newly developed minimally invasive lipid extraction method on parchment, sacrificial pieces of parchments were subjected to artificial ageing and investigated using various analytical methods. Lipids were extracted using our novel vacuum-aided extraction method and characterised by high-temperature gas chromatography (HTGC-FID). Lipids were identified as arising from degraded animal fats. The physical, molecular, and mechanical properties of the parchment samples before/after lipid extraction, and before/after ageing were assessed using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and pure shear single notch fracture testing. SEM imaging allowed for an assessment of potential structural changes of the collagen fibres while FTIR was used to investigate the possible molecular changes indicated by changes in amide I and II bands. Mechanical tests were used to record the changes in brittleness and stiffness occurring in the materials through lipid extraction and ageing. The multimodal investigation did not highlight measurable changes in the structural, molecular, and mechanical properties of the lipid-extracted parchment, thus indicating the suitability for the minimally invasive lipid extraction method to be applied to historical parchments.

An efficient PCN-224/graphene aerogel-based extraction method for monitoring the degradation of organophosphorus pesticides in juice

An efficient PCN-224/graphene aerogel modified silica (PCN-224/GA@Sil)-based extraction method was established for monitoring the degradation process of two organophosphorus pesticides (OPPs) in juice. PCN-224/GA@Sil exhibited higher surface area (307.35 m2 g-1) than graphene oxide modified silica (254.09 m2 g-1). The introduction of PCN-224 endowed the sorbent with excellent adsorption specificity towards OPPs due to the ZrˑˑˑS/O coordination bond. PCN-224 exhibited relatively higher theoretical adsorption energies of PCN-224 towards fenitrothion and fenthion were 0.68 eV and -0.31 eV. The established PCN-224/GA@Sil-HPLC method showed the linearity of 0.2-500 μg L-1 for analytes. The matrix effects in juice were 9.68 % and 3.61 % for fenitrothion and fenthion. Finally, it was used for the sample pretreatment of juice preventing interference from food matrices to monitor the degradation of two OPPs. A combination method of ultrasound and xenon lamp was adopted to degrade fenitrothion and fenthion displaying the synergistic effect (SE=2.12, 1.39).

Anthocyanin Extraction from Roselle (Hibiscus sabdariffa L.) Calyces: A Microwave-Assisted Approach Using Box-Behnken Design

Roselle (Hibiscus sabdariffa L.) is a widely consumed edible flower, often utilized as a natural coloring agent. The bright red color of roselle is due to its anthocyanin content. Beyond their role as colorants, anthocyanins are known for their potent antioxidant properties, which have drawn significant interest for their potential health benefits. To fully harness the advantages of roselle's anthocyanins, developing a reliable extraction method is crucial. This study aimed to establish a fast, efficient, and environmentally friendly extraction technique using microwave-assisted extraction (MAE). Key factors, such as temperature (30, 65, and 100 °C), solvent concentration (30%, 50%, and 70% ethanol in water), and solvent-to-solid ratio (10:1, 25:1, 40:1), were optimized using the Box-Behnken design combined with response surface methodology. The extraction time (5-30 minutes) was also evaluated to maximize anthocyanin recovery. Two major anthocyanins, delphinidin-3-sambubioside and cyanidin-3-sambubioside, were identified using UHPLC-PDA-QToF-MS and quantified using a cyanidin-3-glucoside calibration curve. The optimal extraction conditions were found to be 72 °C, using 70% ethanol in water with a 40:1 solvent-to-solid ratio for 15 minutes. This method was validated with high accuracy (>98% recovery) and precision (CV <6%). It was successfully applied to quantify the anthocyanin content in both purple and red roselle varieties. This rapid and effective MAE method offers a more sustainable, energy-efficient, and time-saving alternative to conventional extraction methods, benefiting industries that rely on natural colorants and antioxidants.

A Simple and Cost-Effective Extraction for Piscine Environmental DNA Metabarcoding Using Guanidine Hydrochloride Method.

Environmental DNA (eDNA) metabarcoding is a valuable tool for assessing aquatic biodiversity, but the high cost and complexity of DNA extraction pose challenges for widespread adoption, especially in developing countries. This study presents a cost-effective eDNA extraction method using a guanidine hydrochloride (GuHCl) buffer, proteinase-K digestion, and isopropanol precipitation to improve the detection of fish communities. Comparison with the Qiagen DNeasy Blood & Tissue Kit using MiFish universal primers showed that the GuHCl protocol detected more fish species in freshwater samples, with comparable performance in relative read abundance metrics. However, the GuHCl method exhibited higher PCR inhibition in brackish samples, likely due to salinity and natural inhibitors. The results suggest that the GuHCl-based method is a viable alternative, offering enhanced sensitivity for low-abundance species in freshwater samples and cost savings. This protocol facilitates large-scale eDNA metabarcoding for ecological studies and conservation management efforts.•The GuHCl protocol identified a greater diversity of fish species in freshwater samples than the Qiagen kit, but detected fewer species in brackish water samples.•Both extraction methods demonstrated robust positive correlations in metrics of relative read abundance.

Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis.

Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. First, a typical machine learning model requires a large number of training samples that physical systems can not provide. Second, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction.

Automated multi-document summarization using extractive-abstractive approaches

This study presents a multi-document text summarizing system that employs a hybrid approach, including both extractive and abstractive methods. The goal of document summarizing is to create a coherent and comprehensive summary that captures the essential information contained in the document. The difficulty in multi-document text summarization lies in the lengthy nature of the input material and the potential for redundant information. This study utilises a combination of methods to address this issue. This study uses the TextRank algorithm as an extractor for each document to condense the input sequence. This extractor is designed to retrieve crucial sentences from each document, which are then aggregated and utilised as input for the abstractor. This study uses bidirectional and auto-regressive transformers (BART) as an abstractor. This abstractor serves to condense the primary sentences in each document into a more cohesive summary. The evaluation of this text summarizing system was conducted using the ROUGE measure. The research yields ROUGE R1 and R2 scores of 41.95 and 14.81, respectively.

The property differences of essential oils extracted by two methods from Dolichos lablab flowers: chemical composition, anti-microorganism and cholinesterase inhibition activities

In this study, the essential oils of Dolichos lablab (syn. Lablab purpureus) flowers (EOFs) were extracted by enzymatic pretreatment plus microwave extraction (EP-ME) method, and the key factors affecting yield of essential oils were investigated. The kinetic curves of essential oils extracted by EP-ME method were fitted, and the results showed that the first-order kinetic model was more appropriate for essential oils extracted by EP-ME method. Under the optimal extraction conditions, EP-ME method not only had high extraction yield, but also had low consumption time. The yield of essential oil extracted by EP-ME method (0.41 ± 0.021% g/g) was 51.9% higher than that by traditional hydro-distillation (HD) method (0.27 ± 0.013% g/g), and the time and energy consumption were 53.33% and 18.62% of the HD method, respectively. The results of GC-MS showed that the contents of main substances in the essential oils by EP-ME method were remarkably increased, including hexadecanoic acid methyl ester (+11.05%), 9Z,12Z,15Z-octadecatrienoic acid methyl ester (+5.26%), (Z)-9-octadecenoic acid methyl ester (+3.85%), methyl hexadic-9-enoate (+3.11%), methyl linoleate (+2.68%), linalool (+1.83%), and methyl stearate (+1.18%). In vitro activity results revealed that EOFs extracted using EP-ME method not only had certain anti-microorganism activity, but also possessed good cholinesterase inhibition activity, and its semi-inhibition concentrations (IC50) against acetyl-cholinesterase and butyrylcholinesterase were 2.48 and 2.31 mg/mL, respectively. Therefore, the EP-ME method was a kind of green and effective method for extracting high-quality essential oils from Dolichos lablab flowers.

Ultrasound-assisted extraction of triterpenoids from Chaenomeles speciosa leaves: Process optimization, adsorptive enrichment, chemical profiling, and protection against ulcerative colitis.

For the valorization of Chaenomeles speciosa leaves, this study focused on extraction, enrichment, chemical profiling, and investigation of the biological activity of its abundant triterpenoid components. Initially, the total triterpenoids in C. speciosa leaves were extracted by ultrasonic-assisted extraction (UAE) method, with the extraction process optimized through response surface methodology (RSM). Under the optimal conditions of extraction solvent 93% EtOH, ultrasound power 390W, extraction time 30min, extraction temperature 70°C, liquid-to-solid ratio 25mL/g, and 2 extraction cycles, the maximum total triterpenoids yield (TTY) reached 36.77±0.40mg/g. The total triterpenoids in the crude extract were subsequently enriched by X-5 resin column chromatography, resulting in a fourfold increase in purity, reaching 73.27±0.84%. Thirteen compounds in the triterpenoid-rich fraction (TRF) were identified through UPLC-QTOF-MS/MS, and five major triterpenoids (oleanolic acid, ursolic acid, betulinic acid, maslinic acid, and pomolic acid) were simultaneously quantified by HPLC-QQQ-MS. Furthermore, TRF demonstrated a notable amelioration against dextran sodium sulfate (DSS)-induced ulcerative colitis in mice, indicating its promise as a potent intervention for this condition. In summary, this study will contribute to enhancing the utilization efficiency of Chaenomeles speciosa leaves.

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography and biopsy are laborious, invasive, and inefficient; confocal microscopy offers many benefits over these approaches. However, confocal microscopy enables the exact differentiation of malignant cells, the expeditious examination of extensive tissue sections, and the optical sectioning of tissue samples into tiny slices. The primary goal should be to prevent cancer altogether, although detecting it early can help achieve that objective. This research presents a novel Breast Histopathology Convolutional Neural Network (BHCNN) for feature extraction and recursive feature elimination method for selecting the most significant features. The proposed approach utilizes full slide images to identify tissue in regions affected by invasive ductal carcinoma. In addition, a transfer learning approach is employed to enhance the performance and accuracy of the models in detecting breast cancer, while also reducing computation time by modifying the final layer of the proposed model. The results showed that the BHCNN model outperformed other models in terms of accuracy, achieving a testing accuracy of 98.42% and a training accuracy of 99.94%. The confusion matrix results show that the IDC positive (+) class achieved 97.44% accuracy and 2.56% inaccurate results, while the IDC negative (-) class achieved 98.73% accuracy and 1.27% inaccurate results. Furthermore, the model achieved less than 0.05 validation loss. RESEARCH HIGHLIGHTS: The objective is to develop an innovative framework using ultra-fast fluorescence confocal microscopy, particularly for the challenging problem of breast cancer diagnosis. This framework will extract essential features from microscopy and employ a gradient recurrent unit for detection. The proposed research offers significant potential in enhancing medical imaging through the provision of a reliable and resilient system for precise diagnosis of breast cancer, thereby propelling the progression of state-of-the-art medical technology. The most suitable feature was determined using BHRFE optimization techniques after retrieving the features by proposed model. Finally, the features chosen are integrated into a proposed methodology, which is then classified using a GRU deep model. The aforementioned research has significant potential to improve medical imaging by providing a complex and reliable system for precise evaluation of breast cancer, hence advancing the development of cutting-edge medical technology.

Enhancing soil organic carbon prediction of LUCAS soil database using deep learning and deep feature selection

The main terrestrial carbon (C) fraction is soil organic carbon (SOC), which has a considerable effect on climate change and greenhouse gas emissions through the absorption and sequestration of carbon dioxide (CO2). This has made SOC assessment very important from both economic and environmental viewpoints. The growing count of soil spectral libraries (SSLs) from regional to global scales has brought a tremendous opportunity for the quantification of SOC through developing spectral-based prediction models. Hence, there is a need to take advantage of big data analytics for spectral data processing. The unique ability of deep learning (DL) techniques to leverage important features of high-dimensional large-scale SSLs has made them top-demanding for more sophisticated modeling. The core objective of the present study was to assess the ability of two different DL algorithms, i.e., one-dimensional convolutional neural network (1DCNN) and fully connected neural network (FCNN) coupled with stacked autoencoder (SAE) feature extraction for SOC prediction based on the data from the land use/cover area frame statistical survey (LUCAS) database. SAE extracted the high-level deep features from the visible–near-infrared–shortwave infrared (Vis–NIR–SWIR) spectra of 11441 soil samples, which were then considered as inputs to the 1DCNN and FCNN models for predicting the SOC content. Both SAE-DL feature-selected models yielded higher accuracy than those the DL developed on the entire spectra and a random forest (RF) model was constructed for comparison. The best prediction was achieved by SAE-1DCNN (R2= 0.78, RMSE = 3.94%, RPD = 4.88, RPIQ = 3.91) followed by 1DCNN (R2= 0.73, RMSE = 5.43%, RPD = 3.67, RPIQ = 2.84) proving the superiority of 1DCNN over FCNN in this study. These results supported the applicability of combined deep features extraction and regression methods for predicting SOC using high dimensional large-scale SSLs.

A Simple, Cost-Effective, and Sustainable Method for Extraction and Quantification of Chebulagic Acid from Terminalia bellirica (Gaertn.) Roxb. Using RP-HPLC: A Comparative Study

Chebulagic acid (CA), a hydrolysable tannin found in Terminalia bellirica (T. bellirica) (Gaertn.) Roxb., has gained prominence as a significant component in Ayurvedic medicine. This study optimized methods for determining CA in T. bellirica, focusing on fruit samples and prioritizing simplicity, cost-effectiveness, less time duration, and sustainability. We evaluated various extraction techniques, including continuous shaking, microwave assisted extraction, and Soxhlet extraction using green solvents. Quantification of CA was conducted using Reverse Phase High-Performance Liquid Chromatography (RP-HPLC), with confirmation achieved via UV-Visible (UV-Vis) spectroscopy, Thin Layer Chromatography (TLC), and Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS). Results indicated that Soxhlet and microwave significant extraction methods, particularly with ethanol as the green solvent. Soxhlet extraction yielded 49607.73±18.82 μg/mL for fresh fruit and 42952.53±19.09 μg/mL for dry fruit, while microwave extraction resulted in 43627.56±113.48 μg/mL for fresh fruit and 40466.33±130.90 μg/mL for dry fruit respectively. These findings conclude that ethanol as a green solvent in Soxhlet or microwave assisted extraction emerge as most efficient, straightforward, and effective approach for detecting and screening CA in T. bellirica samples.

Document-level multiple relations extraction method via evidence guidance and relation correlation

Document-level Relation Extraction (DocRE) aims to extract semantic relations between entity pairs, spanning multiple sentences, paragraphs or even the entire document. These relations can often be predicted by partial sentences within the document, the evidence sentence. However, the relation derived only from sentence information is incomplete, because it ignores the case of multiple relations between entity pairs. Therefore, how to select effective evidence sentences and how to predict multiple relations more accurately have become challenges for the existing DocRE models. In response to these challenges, we introduce Reinforcement Learning (RL) to select more effective evidence sentences, while using heuristic rules to narrow down the search space of RL. Secondly, we utilize GAT to acquire the features of co-occurrence relations, which can greatly improve multiple relations prediction performance. Moreover, the combination of the features of co-occurrence relations and the evidence sentence information enables our method to achieve both high effectiveness and precision. The experimental results show that, compared with other advanced methods, our method achieves an F1 score of 66.56 and the EviF1 score of 56.69, which attains the state-of-the-art performance on public datasets.

Application of deep eutectic solvent based dispersive liquid–liquid microextraction method followed by HPLC-DAD for quantification of selected neonicotinoid pesticides in vegetable oils

The use of deep eutectic solvents (DES) as alternative green solvents in various analytical chemistry fields has garnered attention of researchers globally. Therefore, this study presents environmentally friendly DES for extraction of selected neonicotinoids (acetamiprid, imidacloprid, thiacloprid and thiamethoxam) prior to their determination using high-performance liquid chromatography- diode array detector (HPLC-DAD). The DES mixture that resulted in the highest extraction recoveries of neonicotinoids (NEOs) was choline chloride with ethylene glycol (CCl:EG) and nuclear magnetic resonance and Fourier transform infrared confirmed the successful synthesized CCl:EG. Thereafter, the CCl:EG was applied in dispersive liquid–liquid microextraction (DLLME) for the preconcentration and extraction of acetamiprid, imidacloprid, thiacloprid and thiamethoxam. The optimum factors affecting extraction efficiency of the DES-DLLME procedure were 0.8 mL (DES volume), 4.5 min (extraction time), 0.5 mL (eluent volume), 7 pH and 75 s (eluent time). The optimized DES-DLLME achieved limit of detection (LOD) and quantification (LOD) range of 0.4–4.95 ng.µL−1 and 1.43–9.70 ng.µL−1 respectively. Additionally, the extraction method achieved good accuracy (79.0–119.6 %), preconcentration factors (39.3–118), interday (0.61–1.62 %) and intraday (0.1–0.98 %) precisions. The greenness assessment of the proposed DES-DLLME method using AGREE, NEMI and AES tools revealed that the method was green. Finally, the developed DES-DLLME method was applied to real vegetable oils and the concentrations were below detection limits.

Surface feature extraction method for cloud data of aircraft wall panel measurement points

In the cloud, users need to connect to the data server to perform the file transmission via the Internet, and the Server transmits data to many servers. A machine or vehicle that can fly with the assistance of the air is known as an Aircraft. As an alternative to the downward thrust of jet engines, it uses either static lift or an airfoil's dynamic lift to combat gravity's pull. Drawing wall panel measurement points in the model is easy using the Aircraft Wall Panels (AWP) button. Draw wall panels between existing nodes or on the drawing grid using the relevant wall panel specifications. The technique intends to discover and extract information about undesirable defects such as dents, protrusions, or scratches based on local surface attributes gathered from a 3D scanner. Defects from a perfectly smooth surface include indentations and bumps on the surface. An image's features may be extracted by reducing the number of pixels in the picture to a manageable size so that the most exciting sections of the image can be recorded with Surface Feature Extraction (SFE). Some of the problems are the threat of drones and composite materials that do not break easily in oxymoronic. The aircraft's inner structure may have been damaged, although this is impossible to determine. A runway incursion severely threatens aviation safety because of the rise in aircraft movement on the airport surface and other human factors. An electronic moving map of airport runways and taxiways is shown to the pilot through a head-up display in the cockpit's head-down position. A practical feature extraction approach is required to ensure the safety of the airport scene in runway incursion prevention systems. All the drawbacks are rectified by AWP-SFE sensors installed along the runway centerline to detect magnetic signals generated by surface-moving targets, and this information is utilized to compute the target's length. The target length may extract peak features after regularizing the time domain data. Differentiation of target characteristics is used to determine the similarities between distinct targets. The suggested method's signal characteristics are more easily recognized than time domain or frequency domain feature methods. The experimental results show the proposed method AWP-SE to achieve a high-efficiency ratio of 88.2%, activity ratio of 73.3%, Analysis of aircraft in wall plane measurement point of 87.8% and an error rate of 32.3% compared to other methods.

Enhancement and Validation of a Quantitative GC-MS Method for the Detection of ∆9-THC and THC-COOH in Postmortem Blood and Urine Samples

Cannabis is frequently detected in forensic investigations and is associated with an increased risk of fatal car crashes. This study aims to develop a method to detect and measure ∆9-tetrahydrocannabinol (∆9-THC) in blood and its metabolite, 11-nor-9-tetrahydrocannabinol-carboxylic acid (∆9-THC-COOH), in urine. The procedure employs two liquid-liquid extraction methods in conjunction with GC-MS in SIM mode. Both compounds were successfully processed, demonstrating the method's ease of use and efficiency. The method was validated for selectivity, identification capability, linearity, precision, limit of detection (LOD), limit of quantification (LOQ), and accuracy. Its effectiveness was further demonstrated by applying it to 30 authentic urine and blood samples from cannabis-related cases, establishing it as a valuable option for routine cannabinoid analysis in forensic toxicology labs.•The linearity range was 25–300 ng/mL for ∆9-THC in blood, and 50–300 ng/mL for ∆9-THC-COOH in urine, and calibration curves for both analytes showed R² values consistently above 0.989, confirming their linearity.•The LOD and LOQ for THC-COOH in hydrolyzed urine were 25 ng/mL and 50 ng/mL, respectively, and for THC in blood, they were 15 ng/mL and 25 ng/mL, respectively.•The variation coefficients were below 14%, and recoveries exceeded 81% for both compounds.